Postural stability – a comparison between rowers and field sport athletes

Postural stability (PS) is an important function for maintaining equilibrium during periods of standing still, locomotion, and any motor activities that require high degree of balance. High PS is essential in different sports for the regulation of voluntary movement and for improving athletic physical condition and performance. Purpose: The purpose of this study was to compare the static PS of elite rowing athletes and field sport athletes. Methods: A total of 90 elite athletes (age: 23.9 ± 1.97 years; body height: 174.9 ± 8.9 cm; body weight: 67.7 ± 12.03 kg) were divided into Rowing (N = 47) and Field sport (N = 43) athlete groups. Static PS parameters were assessed with a static double-leg and single-leg standing stability test on a force plate platform. Results: The multivariate analysis of variance showed a general stability difference between the groups (F = 13.255; P ≤ 0.0001), in double leg stability (F = 16.735; P ≤ 0.0001), and left leg (F = 15.097; P ≤ 0.0001) stability parameters. When analyzing variables separately, significant statistical differences were observed in favor of the Rowing group in double leg sway area (p = 0.017; ES = −0.07), double leg center of force (COF) traveled way (p ≤ 0.0001; ES = −27.42), length function of surface (p ≤ 0.0001; ES = −26.86), right leg ML displacement (p = 0.030; ES = −0.46), left leg sway area (p = 0.030; ES = −0.44), left leg COF traveled way (p ≤ 0.0001; ES = −60.63), left leg AP displacement (p = 0.043; ES = −0.44). Conclusion: These results underline the differences in rowing and field sport athletes in terms of static PS. The characteristics of sport and competition may affect PS, and it is important to adjust training modalities for the required level of PS in every sport, especially in rowing

Viewing the efficiency of chaos control

This paper aims to cast some new light on controlling chaos using the OGY- and the Zero-Spectral-Radius methods. In deriving those methods we use a generalized procedure differing from the usual ones. This procedure allows us to conveniently treat maps to be controlled bringing the orbit to both various saddles and to sources with both real and complex eigenvalues. We demonstrate the procedure and the subsequent control on a variety of maps. We evaluate the control by examining the basins of attraction of the relevant controlled systems graphically and in some cases analytically

Replica theory for learning curves for Gaussian processes on random graphs

Statistical physics approaches can be used to derive accurate predictions for the performance of inference methods learning from potentially noisy data, as quantified by the learning curve defined as the average error versus number of training examples. We analyse a challenging problem in the area of non-parametric inference where an effectively infinite number of parameters has to be learned, specifically Gaussian process regression. When the inputs are vertices on a random graph and the outputs noisy function values, we show that replica techniques can be used to obtain exact performance predictions in the limit of large graphs. The covariance of the Gaussian process prior is defined by a random walk kernel, the discrete analogue of squared exponential kernels on continuous spaces. Conventionally this kernel is normalised only globally, so that the prior variance can differ between vertices; as a more principled alternative we consider local normalisation, where the prior variance is uniform

Disorder Predictors Also Predict Backbone Dynamics for a Family of Disordered Proteins

Several algorithms have been developed that use amino acid sequences to predict whether or not a protein or a region of a protein is disordered. These algorithms make accurate predictions for disordered regions that are 30 amino acids or longer, but it is unclear whether the predictions can be directly related to the backbone dynamics of individual amino acid residues. The nuclear Overhauser effect between the amide nitrogen and hydrogen (NHNOE) provides an unambiguous measure of backbone dynamics at single residue resolution and is an excellent tool for characterizing the dynamic behavior of disordered proteins. In this report, we show that the NHNOE values for several members of a family of disordered proteins are highly correlated with the output from three popular algorithms used to predict disordered regions from amino acid sequence. This is the first test between an experimental measure of residue specific backbone dynamics and disorder predictions. The results suggest that some disorder predictors can accurately estimate the backbone dynamics of individual amino acids in a long disordered region

Subfunctionalization reduces the fitness cost of gene duplication in humans by buffering dosage imbalances

<p>Abstract</p> <p>Background</p> <p>Driven essentially by random genetic drift, subfunctionalization has been identified as a possible non-adaptive mechanism for the retention of duplicate genes in small-population species, where widespread deleterious mutations are likely to cause complementary loss of subfunctions across gene copies. Through subfunctionalization, duplicates become indispensable to maintain the functional requirements of the ancestral locus. Yet, gene duplication produces a dosage imbalance in the encoded proteins and thus, as investigated in this paper, subfunctionalization must be subject to the selective forces arising from the fitness bottleneck introduced by the duplication event.</p> <p>Results</p> <p>We show that, while arising from random drift, subfunctionalization must be inescapably subject to selective forces, since the diversification of expression patterns across paralogs mitigates duplication-related dosage imbalances in the concentrations of encoded proteins. Dosage imbalance effects become paramount when proteins rely on obligatory associations to maintain their structural integrity, and are expected to be weaker when protein complexation is ephemeral or adventitious. To establish the buffering effect of subfunctionalization on selection pressure, we determine the packing quality of encoded proteins, an established indicator of dosage sensitivity, and correlate this parameter with the extent of paralog segregation in humans, using species with larger population -and more efficient selection- as controls.</p> <p>Conclusions</p> <p>Recognizing the role of subfunctionalization as a dosage-imbalance buffer in gene duplication events enabled us to reconcile its mechanistic nonadaptive origin with its adaptive role as an enabler of the evolution of genetic redundancy. This constructive role was established in this paper by proving the following assertion: <it>If subfunctionalization is indeed adaptive, its effect on paralog segregation should scale with the dosage sensitivity of the duplicated genes</it>. Thus, subfunctionalization becomes adaptive in response to the selection forces arising from the fitness bottleneck imposed by gene duplication.</p

Rules Governing Selective Protein Carbonylation

BACKGROUND:Carbonyl derivatives are mainly formed by direct metal-catalysed oxidation (MCO) attacks on the amino-acid side chains of proline, arginine, lysine and threonine residues. For reasons unknown, only some proteins are prone to carbonylation. METHODOLOGY/PRINCIPAL FINDINGS:we used mass spectrometry analysis to identify carbonylated sites in: BSA that had undergone in vitro MCO, and 23 carbonylated proteins in Escherichia coli. The presence of a carbonylated site rendered the neighbouring carbonylatable site more prone to carbonylation. Most carbonylated sites were present within hot spots of carbonylation. These observations led us to suggest rules for identifying sites more prone to carbonylation. We used these rules to design an in silico model (available at http://www.lcb.cnrs-mrs.fr/CSPD/), allowing an effective and accurate prediction of sites and of proteins more prone to carbonylation in the E. coli proteome. CONCLUSIONS/SIGNIFICANCE:We observed that proteins evolve to either selectively maintain or lose predicted hot spots of carbonylation depending on their biological function. As our predictive model also allows efficient detection of carbonylated proteins in Bacillus subtilis, we believe that our model may be extended to direct MCO attacks in all organisms

Predicting mostly disordered proteins by using structure-unknown protein data

BACKGROUND: Predicting intrinsically disordered proteins is important in structural biology because they are thought to carry out various cellular functions even though they have no stable three-dimensional structure. We know the structures of far more ordered proteins than disordered proteins. The structural distribution of proteins in nature can therefore be inferred to differ from that of proteins whose structures have been determined experimentally. We know many more protein sequences than we do protein structures, and many of the known sequences can be expected to be those of disordered proteins. Thus it would be efficient to use the information of structure-unknown proteins in order to avoid training data sparseness. We propose a novel method for predicting which proteins are mostly disordered by using spectral graph transducer and training with a huge amount of structure-unknown sequences as well as structure-known sequences. RESULTS: When the proposed method was evaluated on data that included 82 disordered proteins and 526 ordered proteins, its sensitivity was 0.723 and its specificity was 0.977. It resulted in a Matthews correlation coefficient 0.202 points higher than that obtained using FoldIndex, 0.221 points higher than that obtained using the method based on plotting hydrophobicity against the number of contacts and 0.07 points higher than that obtained using support vector machines (SVMs). To examine robustness against training data sparseness, we investigated the correlation between two results obtained when the method was trained on different datasets and tested on the same dataset. The correlation coefficient for the proposed method is 0.14 higher than that for the method using SVMs. When the proposed SGT-based method was compared with four per-residue predictors (VL3, GlobPlot, DISOPRED2 and IUPred (long)), its sensitivity was 0.834 for disordered proteins, which is 0.052–0.523 higher than that of the per-residue predictors, and its specificity was 0.991 for ordered proteins, which is 0.036–0.153 higher than that of the per-residue predictors. The proposed method was also evaluated on data that included 417 partially disordered proteins. It predicted the frequency of disordered proteins to be 1.95% for the proteins with 5%–10% disordered sequences, 1.46% for the proteins with 10%–20% disordered sequences and 16.57% for proteins with 20%–40% disordered sequences. CONCLUSION: The proposed method, which utilizes the information of structure-unknown data, predicts disordered proteins more accurately than other methods and is less affected by training data sparseness

The utility of Tc-99m-EDDA/HYNIC-TOC scintigraphy for assessment of lung lesions in patients with neuroendocrine tumors

Our aim was to assess clinical utility of Tc-99m-EDDA/HYNIC-TOC scintigraphy for evaluation of lung lesions in patients with neuroendocrine tumors (NETs). Single photon emission computed tomography (SPECT) of the thorax and whole body scintigraphy were performed in 34 patients using Tc-99m-EDDA/HYNIC-TOC. Visual assessment was complemented by semiquantitative evaluation based on tumor to non-tumor (TINT) ratio. Clinical, laboratory, and histological findings served as the standard for comparison. Enhanced tracer uptake was observed on both SPECT and whole body scintigraphy in 29 of 34 patients (88% sensitivity). TINT ratios were significantly higher on SPECT than whole body images (2.96 +/- 1.07 vs. 1.70 +/- 0.43, p LT 0.01) and did not correlate with NET proliferation index Ki-67 (r= - 0.36, p=0.27). Conclusion: Tc-99m-EDDA/HYNIC-TOC scintigraphy is useful for evaluation of NET tissue in the lungs. SPECT provides better visualization of lung lesions than whole body scintigraphy. The intensity of tracer uptake, however, does not relate to the proliferation rate of NETs. Tc-99m-EDDA/HYNIC-TOC scintigraphy may be helpful for selecting and monitoring treatment options, particularly when radiolabeled somatostatin analogue therapy becomes available

Direct targeting of hippocampal neurons for apoptosis by glucocorticoids is reversible by mineralocorticoid receptor activation

Prova tipográfica (In Press)An important question arising from previous observations in vivo is whether glucocorticoids can directly influence neuronal survival in the hippocampus. To this end, a primary postnatal hippocampal culture system containing mature neurons and expressing both glucocorticoid (GR) and mineralocorticoid (MR) receptors was developed. Results show that the GR agonist dexamethasone (DEX) targets neurons (microtubule-associated protein 2-positive cells) for death through apoptosis. GR-mediated cell death was counteracted by the MR agonist aldosterone (ALDO). Antagonism of MR with spironolactone ([7a-(acetylthio)-3-oxo-17a-pregn- 4-ene,21 carbolactone] (SPIRO)) causes a dose-dependent increase in neuronal apoptosis in the absence of DEX, indicating that nanomolar levels of corticosterone present in the culture medium, which are sufficient to activate MR, can mask the apoptotic response to DEX. Indeed, both SPIRO and another MR antagonist, oxprenoate potassium ((7a,17a)-17-Hydroxy-3-oxo-7- propylpregn-4-ene-21-carboxylic acid, potassium salt (RU28318)), accentuated DEX-induced apoptosis. These results demonstrate that GRs can act directly to induce hippocampal neuronal death and that demonstration of their full apoptotic potency depends on abolition of survival-promoting actions mediated by MR